System requirements

Functional:

1. Data Ingestion:

• Support for ingesting real-time streaming data and batch data from various sources.
• Handling of diverse data formats including structured, semi-structured, and unstructured data.
• Scalable ingestion process to handle high data volumes efficiently.

1. Data Processing:

• Transformation and normalization of incoming data.
• Enrichment of data by combining multiple sources.
• Real-time data processing for immediate insights.
• Batch processing for historical analysis.

1. Data Storage:

• Scalable storage solution for both real-time and historical data.
• Support for structured and unstructured data.
• Data partitioning and indexing for efficient data retrieval.

1. Data Analysis:

• Integration with analytics tools for generating actionable insights.
• Ability to run complex queries on the stored data.
• Integration with visualization tools for reporting.

1. Data Quality:

• Data validation to ensure accuracy and integrity.
• Data cleansing to handle missing or inconsistent data.
• Monitoring data quality throughout the pipeline.

1. Scalability and Performance:

• Horizontal scalability to handle increasing data volumes.
• Low latency processing to deliver real-time insights.
• Load balancing to distribute data processing tasks efficiently.

Non-Functional:

1. Reliability:

• Ensure the system is highly available and resilient to failures.
• Implement redundancy and failover mechanisms to minimize downtime.

1. Performance:

• Optimize processing performance to handle large data volumes with low latency.
• Minimize processing delays to deliver real-time insights.

1. Security:

• Implement data encryption in transit and at rest to ensure data security.
• Enforce access controls and authentication mechanisms to prevent unauthorized access.

1. Scalability:

• Design the system to scale horizontally to accommodate growing data volumes and user loads.
• Ensure scalability across all layers of the pipeline, including ingestion, processing, storage, and analysis.

1. Maintainability:

• Ensure the system is easy to maintain and update with minimal downtime.
• Provide comprehensive documentation and logging for troubleshooting and maintenance purposes.

1. Compliance:

• Ensure compliance with data privacy regulations and industry standards.
• Implement auditing and logging mechanisms to track data access and processing activities for compliance purposes.

Capacity estimation

Estimate the scale of the system you are going to design...

API design

Define what APIs are expected from the system...

Database design

Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...

High-level design

1. Data Sources:

• Description: Various sources such as databases, IoT devices, sensors, log files, social media feeds, etc., from where the data originates.
• Functionality: Provide raw data to the ingestion layer for processing.
• Examples: Relational databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra), IoT devices, Web APIs, log files.

2. Data Ingestion Layer:

• Description: Responsible for ingesting data from different sources in real-time or batches.
• Functionality: Collect data from various sources, transform it into a common format, and deliver it to the processing layer.
• Examples: Apache Kafka, Apache NiFi, AWS Kinesis, Google Cloud Pub/Sub.

3. Data Processing Layer:

• Description: Handles data transformation, enrichment, filtering, and aggregation.
• Functionality: Processes raw data to derive insights and prepare it for storage and analysis.
• Examples: Apache Spark, Apache Flink, Hadoop MapReduce.

4. Data Storage Layer:

• Description: Stores both real-time and historical data for further analysis.
• Functionality: Provides scalable and durable storage solutions to accommodate growing data volumes.
• Examples: Hadoop HDFS, Amazon S3, Google Cloud Storage, Azure Data Lake Storage.

5. Data Analysis Layer:

• Description: Performs analytics on the stored data to derive insights and patterns.
• Functionality: Runs queries and algorithms to extract meaningful information from the data.
• Examples: Apache Hive, Apache Pig, Apache Drill, SQL databases.

6. Data Visualization Layer:

• Description: Presents the analyzed data in visually comprehensible formats.
• Functionality: Creates charts, graphs, and dashboards to visualize insights and trends.
• Examples: Tableau, Power BI, Grafana, Kibana.

7. Monitoring and Logging:

• Description: Monitors the health and performance of the pipeline components.
• Functionality: Tracks data processing activities, errors, and system metrics for troubleshooting and optimization.
• Examples: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), Splunk.

8. Security and Compliance:

• Description: Ensures data security, privacy, and compliance with regulations.
• Functionality: Implements encryption, access controls, and auditing mechanisms to protect sensitive data.
• Examples: Encryption tools, Access Control Lists (ACLs), auditing solutions.

Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...

Detailed component design

Data Ingestion Layer:

1. Purpose:
2. The Data Ingestion Layer is responsible for collecting data from various sources, transforming it into a common format, and delivering it to the processing layer.
3. Usually a data ingestion product like azure data factory, azure synapse or aws kinesis is chosen to pull data from all sources.
4. Data Sources:
5. Databases: Relational databases (e.g., MySQL, PostgreSQL), NoSQL databases (e.g., MongoDB, Cassandra).
6. IoT Devices: Sensors, smart devices, machines generating sensor data.
7. Logs: Server logs, application logs, event logs.
8. Streaming Platforms: Social media feeds, clickstream data, sensor data streams.
9. File Systems: CSV files, JSON files, XML files, Parquet files.
10. Data Ingestion Methods: Data can arrive in the system in 2 ways either in batches where data is pulled using api calls or data is often pushed to a database or Real-time - this data can come from multiple devices or social media platforms or partner servers or telemetry systems.
11. Batch Ingestion:
12. Pulling data from databases using JDBC/ODBC connections.
13. Utilizes batch processing frameworks like Apache Hadoop or Apache Spark for processing large volumes of data in batches.
14. Reading log files from file systems using file readers.
15. Periodic extraction and loading of data from sources like APIs or file systems.
16. Real-time Ingestion:
17. Subscribing to data streams using messaging systems like Kafka or AWS Kinesis.
18. Utilizes real-time processing frameworks like Apache Kafka Streams or Apache Flink for processing data streams in real-time.
19. 
    
20. Streaming data from IoT devices using MQTT or AMQP protocols.
21. Continuous monitoring of log files for real-time event detection.
22. Data Ingestion Schedules: Depending on the type of data, pipelines are scheduled to execute. For data where immediate processing is not required, pipelines are scheduled to execute hourly or daily. For data where immediate extraction and processing is necessary, the ingestion system are running continuously, these systems connect to queue stores or event hubs where events are being pushed.
23. Data Processing and Enrichment:
24. Unified Processing Logic: the processing logic is implemented such that it can handle both batch and real-time data ingestion.
25. Ingestion processing should use common data transformation and enrichment techniques across batch and real-time processing components.

Data Processing Layer

The Data Processing Layer forms the core of any Big Data architecture, serving as the engine that transforms raw data into actionable insights. It encompasses both batch and real-time processing components, enabling organizations to handle diverse data sources and processing requirements. By leveraging technologies like Apache Spark, Apache Flink, and Apache Kafka Streams, the Data Processing Layer orchestrates the ingestion, transformation, and analysis of large volumes of data with efficiency and scalability. Its seamless integration with storage solutions such as data warehouses, data lakes, and operational data stores ensures that processed data is stored appropriately for downstream analytics and decision-making.

Components for Data Processing:

1. Batch Processing Component:
2. Technology: Apache Hadoop, Apache Spark Batch, Apache Flink Batch.
3. Functionality: Processes large volumes of data in fixed-size batches.
4. Use Cases: Handling historical data analysis, ETL (Extract, Transform, Load) jobs, batch reporting.
5. Real-time Processing Component:
6. Technology: Apache Kafka Streams, Apache Flink Streaming, Apache Storm.
7. Functionality: Processes data streams in real-time, providing low-latency processing.
8. Use Cases: Real-time analytics, anomaly detection, event-driven architectures.
9. Unified Processing Logic:
10. Technology: Apache Beam (unified batch and stream processing model).
11. Functionality: Provides a unified programming model for both batch and real-time processing.
12. Use Cases: Developing processing logic that can seamlessly switch between batch and stream modes.

In data processing pipelines, data can undergo multiple processing stages to derive deeper insights or refine analyses iteratively. Each processing stage applies specific transformations or algorithms to the data, refining its quality and relevance. The Medallion data architecture exemplifies this iterative processing approach, where data passes through multiple layers, including raw data ingestion, staging, transformation, and analytics, before being stored in a data warehouse or data lake. At each stage, data is refined and enriched, allowing for iterative refinement of analytical models and the generation of increasingly valuable insights for decision-making.

Data Storage Layer:

The Data Storage Layer serves as the repository for storing both raw and processed data in a structured and accessible manner within a Big Data architecture. It encompasses various storage solutions designed to accommodate the diverse needs of storing and managing large volumes of data efficiently.

Data Warehouse:

A Data Warehouse is a centralized repository that stores structured and organized data from various sources, optimized for analytical querying and reporting. It consolidates data from disparate sources, cleanses and transforms it into a consistent format, and enables business users to perform complex queries for insights and decision-making. In the context of the Medallion architecture, the Data Warehouse serves as a crucial component for storing refined and aggregated data ready for analytics and reporting.

Medallion Architecture:

The Medallion architecture follows a multi-layered approach to data processing and storage, facilitating iterative refinement and analysis of data. Within this architecture, the Data Storage Layer plays a pivotal role in storing data at different stages of processing, including raw data, intermediate data, and refined data. The Medallion architecture emphasizes the importance of data warehouses as a centralized repository for storing processed data, making it accessible for analytics and decision-making across the organization.

With Medallion Architecture data is divided into different layers as below

Bronze Layer:

• The Bronze layer represents the raw, unprocessed data ingested from various sources.
• Data is stored in its original format, including raw logs, unstructured data, and transactional records.
• Minimal processing or transformation is applied, focusing on data integrity and preservation.

Silver Layer:

• The Silver layer is an intermediate stage where raw data is cleansed, transformed, and organized for analysis.
• Data undergoes cleansing, normalization, and enrichment to improve quality and consistency.
• Structured data formats are applied, making it suitable for relational databases or data lakes.

Gold Layer:

• The Gold layer represents the refined and aggregated data ready for advanced analytics and reporting.
• Data is fully refined, aggregated, and optimized for analytical querying and reporting.
• Complex transformations, joins, and calculations are applied to derive actionable insights.
• Data is stored in a data warehouse or analytics-ready storage solution for easy access and analysis.

Data Analysis Layer

Once the data arrives in Gold Layer, multiple consumers can start consuming the data and perform various experiments over it. Processes like Demand Forecasting, Deeper insights using machine learning, recommendation generation etc. can be done using the data.

This layer is similar to Data Processing Layer, where multiple pipelines are created to get the data from Gold Layer and passed on to Machine Learning Algorithms.

For this layer pre-trained models can be used which are available with most cloud service providers. the output of these models can be stored in a different layer. This will be consumed further by Data Visualization Tools and other pipelines.

Data Visualization Layer

The Data Visualization Layer serves as a critical component within a data architecture, transforming complex datasets and analytical findings into intuitive and visually engaging representations. It plays a pivotal role in facilitating understanding, decision-making, and communication of insights derived from data analysis. By leveraging various visualization techniques and tools, organizations can effectively convey patterns, trends, and relationships within data to stakeholders across different levels of expertise. Ultimately, the Data Visualization Layer acts as a bridge between raw data and actionable insights, empowering users to derive meaning and make informed decisions based on data-driven evidence.

Tools like Tableau, Power BI, Graphana etc are used to create various visuals.

Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...

Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.

Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?